Previous Experience and the Learning of Computer Programming: The Computer Helps Those Who Help Themselves

Author:

Kersteen Zoe A.1,Linn Marcia C.1,Clancy Michael1,Hardyck Curtis1

Affiliation:

1. University of California, Berkeley

Abstract

Recent developments in mathematics education indicate that previous experience is the best predictor of high school math achievement scores. Given this information we hypothesized that previous experience with computers would serve as a predictor of performance in college computer science courses. Also of interest was the possible interaction of gender, prior computing experience and computer science course performance. To examine these issues, we designed and administered a questionnaire to students across two semesters of the first year Pascal programming course at the university level. Roughly one-quarter of the students enrolled across the two semesters were female. Results show that males have more prior experience, especially in advanced computer science topics, than females, and that much of this prior experience is gained outside of school through “hacking” and unguided exploration. Amount of prior computing experience was found to predict course performance for males. For females very little prior experience was reported and this limited amount of experience was not predictive of course performance. The question of why women have so little prior experience with computers and are so sparsely represented in computer science courses is addressed.

Publisher

SAGE Publications

Subject

Computer Science Applications,Education

Reference12 articles.

Cited by 35 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Correlating Students' Class Performance Based on GitHub Metrics: A Statistical Study;Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1;2023-06-29

2. GitHub in the Classroom: Lessons Learnt;Australasian Computing Education Conference;2022-02-14

3. Applying social networks to engineering education;Computer Applications in Engineering Education;2018-06-28

4. Interest and Performance When Learning Online;Evolving Psychological and Educational Perspectives on Cyber Behavior;2013

5. Interest and Performance When Learning Online;International Journal of Cyber Behavior, Psychology and Learning;2011-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3